(314a) Computational Analysis of Dynamic Cytokine Signaling Responses by Individual T Cells | AIChE

(314a) Computational Analysis of Dynamic Cytokine Signaling Responses by Individual T Cells


Han, Q. - Presenter, Massachusetts Institute of Technology
Love, J. - Presenter, Massachusetts Institute of Technology

The immune system coordinates a diverse T cell response upon antigenic stimulation to protect the host from disease.  This response involves the release of many cytokines with various immunomodulatory functions.  The efficacy of the immune response depends, in part, on the types of cytokines secreted by activated T cells and their corresponding kinetic profiles.  In order to resolve and predict the contribution of specific T cell subsets to an immunological response, we need to quantitatively measure and computationally analyze the large diversity of cells that comprise the human immune system. 

The Love Lab developed an approach called microengraving that uses an array of microwells to generate microarrays of proteins secreted from large numbers of individual live cells (104–105 cells/assay) [1].  Resulting dynamic measurements quantify single-cell cytokine secretion dynamics, offering a unique multi-dimensional approach to investigate functional differences specific to immunophenotypes.  We employed statistical and systems analysis tools to these data to (i) identify unique dynamic responses, (ii) gain insight on intracellular networks, and (iii) discriminate among cells by means of functional profiles.  By combining quantitative experimental measurements with computational analysis, we can ascertain the contribution of specific cytokine signaling to overall immune function and investigate the regulatory mechanisms involved in the differentiation of immune cells.  As a result, we are able to better investigate, understand, and predict the nonlinear signaling dynamics that govern qualitatively different T-cell performance.  Continued studies may offer metrics to design more effective and personalized treatment strategies.

[1]  Qing Han et al., Lab Chip, 2010, 10: 1391-1400.